The power of real-time, multidimensional insights

We are generating more data today than ever before. But businesses are struggling to gain tangible value from the data they are collecting meaning it is simply taking up server space and costing them money as opposed to delivering value.

For data to become valuable, it needs to be used to deliver insights. Insights that can be used to deliver tangible impact to the business through the improvement of operations, the refinement of processes or the identification and mitigation of risks and problems.

There are two key steps to delivering these valuable insights: (i) they must offer the user information that can be actioned that they don’t know already – in short, the more valuable insights tend to comprise multiple dimensions, and (ii) they must be delivered in near real-time.


What do we mean by multidimensional insights?

Valuable insights tend to be more complex. This typically means a dataset with many columns, also called features or attributes. The more columns in a dataset, the more likely you are to find hidden insights that provide unknown information that can be applied to deliver tangible value to a business. Conversely, the less complex the insight, the more likely the business is already aware.

If the data is structured correctly, the links between various data can be identified and insights can be captured. Think of this data as being in a cube with multiple panes. Each pane is a dimension, and the data is organised in a way that allows patterns etc. to be identified. 

The Entopy platform uses ontology to develop real-time digital twins that reflect the real world, from which insights can be captured through the depiction of entities and the dynamic relationships between them within given contexts: entities (and their many attributes) have dynamic relationships with other entities (and there many attributes) and these relationships change over time. This means Entopy can capture insights that comprise many dimensions (relationships between entities over time, attributes of respective entities with relevance over time, situations of respective entities over time etc.) which uncovers highly valuable insights to be captured. 

The importance of real-time:

Time is a critical component of the value of the insight. In an operational (live) sense, the value of the insight falls dramatically as time passes from the actual event.

But it’s not just the operational value of the insight that deteriorates over time. In many cases, the insight can only be captured in real time. Think of the arrival of a consignment or the conditions of a food court when a customer left. The various strands of data must be pulled together at a specific point in time to generate insight in the first place.